Posted by Martyn R Jones on August 13, 1999 at 17:14:05:

In Reply to: Re: Data Warehouse and Groupware posted by steve on August 13, 1999 at 10:42:24:

Hello Steve,

Here is some very brief info. on the Data Warehouse side of things (taken from our draft manuscript on Information Management in Telecommunications):

2.5 Information Warehouses – A Definition of DW

Bill Inmon, a prolific writer and champion of the Data Warehouse (DW) concept has defined data warehousing as a database containing Subject Oriented, Integrated, Time Variant and Non-volatile information used to support the decision making process. Here we take a closer look at those terms and what they actually mean.

Subject Oriented

Operational databases, such as order processing and payroll databases, are organized around business processes or functional areas. These databases grew out of the applications they served. Thus, the data was relative to the order processing application or the payroll application. Data on a particular subject, such as products or employees, was maintained separately (and usually inconsistently) in a number of different databases. In contrast, a data warehouse is organized around subjects. This subject orientation presents the data in a much easier-to-understand format for end users and non-IT business analysts.

Integrated

Integration of data within a warehouse is accomplished by making the data consistent in format, naming, and other aspects. Operational databases, for historic reasons, often have major inconsistencies in data representations. For example, a set of operational databases may represent "male" and "female" by using codes such as "m" and "f", by "1" and "2", or by "b" and "g". Often, the inconsistencies are more complex and subtle. In a data warehouse, on the other hand, data is always maintained in a consistent fashion.

Time Variant

Data warehouses are time variant in the sense that they maintain both historical and (nearly) current data. Operational databases, in contrast, contain only the most current, up-to-date data values. Furthermore, they generally maintain this information for no more than a year (and often much less). In contrast, data warehouses contain data that is generally loaded from the operational databases daily, weekly, or monthly which is then typically maintained for a period of 3 to 10 years. This is a major difference between the two types of environments.

Historical information is of high importance to decision makers, who often want to understand trends and relationships between data. For example, the product manager for a Liquefied Natural Gas soda drink may want to see the relationship between coupon promotions and sales. This is information that is almost impossible - and certainly in most cases not cost effective - to determine with an operational database.

Non-Volatile

Non-volatility, the final primary aspect of data warehouses, means that after the data warehouse is loaded there are no changes, inserts, or deletes performed against the informational database. The data warehouse is, of course, first loaded with transformed data that originated in the operational databases.

The data warehouse is subsequently reloaded or, more likely, appended on a periodic basis (usually nightly, weekly, or monthly) with new transformed data from the operational databases. Outside of this loading process, the data warehouse generally stays static. Due to non-volatility, the data warehouse can be heavily optimized for query processing.

Hope this helps - by the way, there is a lot of information on DW on the web.

Typically, data in the Data Warehouse will be derived from Operational Systems Database after passing through a process of extraction (from the operational databases), transformation (putting the data into a form that is easier for humans to use), cleaning (applying quality and conformity criterion etc. to data) etc.

My own opinion on DW is that it should only be used for strategic, tactical and (only very occasionally) operational decision support (as in the case of some applications of CRM)

Best regards,

Martyn R Jones

• KM

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Follow Ups:

• Re: Brief defining characteristics of a Data Warehouse Reilly Atkinson 20:09:25 8/13/99 (1)
  • Data Warhousing in Context Martyn R Jones 20:59:47 8/15/99 (0)

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